Road traffic accidents in Italy during COVID-19

The Hidden Pandemic: Impact of the COVID-19 Pandemic on Trauma Cases Due to Domestic Violence Admitted to the Biggest Level-One Trauma Center in Austria

BACKGROUND

An alarming increase in domestic violence was reported during the COVID-19 pandemic worldwide. The aim of this study is to investigate changes in the frequency and the nature of domestic violence at the largest level-one trauma center in Austria.

METHODS

All patients admitted to our institution with domestic violence injuries 15 months before and after the beginning of the COVID-19 pandemic were included. For our analysis, we investigated the frequency of trauma patients after domestic violence in relation to all other trauma patients. Furthermore, age, sex, citizenship, injury pattern, injured body regions, injury mechanism, offender-victim relationship, and hospitalization rate were also analyzed.

RESULTS

Among all trauma patients admitted, the ratio of patients who reported domestic violence injuries increased from 0.465% to 0.548% since the start of the pandemic. In addition, out of the total count of domestic violence victims, the percentage of Austrian citizens increased significantly from 51.2% to 60.6% (p = 0.016). All other parameters showed no significant changes pre and post-pandemic.

CONCLUSION

The COVID-19 pandemic contributed to a relative increase in patients with domestic violence injuries at the largest trauma unit in Austria, along with a significant increase among Austrian citizens. The remaining study parameters did not differ significantly, indicating that the frequency changed during the pandemic but not the underlying pattern of domestic violence.

Impact of COVID-19 on Fracture Incidence in Germany: A Comparative Age and Gender Analysis of Pre- and Post-Outbreak Periods

In March 2020, Germany imposed a nationwide lockdown to curb the spread of COVID-19, prompting questions about the impact on the incidence of common fractures. This study examined 15 fracture types in pre-outbreak (2010-2019) and post-outbreak (2020-2021) periods, using data categorized by age (18-64 years, >65 years) and sex (male, female). Linear regression assessed annual growth rates, and mean fracture numbers were compared across periods for significant differences. Results indicated a positive correlation between fracture incidence rates and time for various types, such as cervical, thoracic, lumbar, and pelvic spine fractures, rib fractures, femoral neck, pertrochanteric femur, femoral shaft, and ankle fractures. Frequencies of proximal humerus, distal radius, femoral neck, pertrochanteric femur, femoral shaft, and ankle fractures in 2020 and 2021 were within predicted ranges from previous years. However, rib fractures and spinal fractures (cervical, thoracic, lumbar, and pelvic spine) occurred less frequently during this time. Notably, this study found a consistent decline in most fracture types for individuals aged 18-64 after the pandemic's onset, while the fracture incidence of hip fractures, often referred to as fragility fractures, for those over 65 remained unchanged. Fibula fractures showed the most considerable decrease in both age groups. In conclusion, the COVID-19 pandemic substantially impacted fracture incidence, with lower rates among individuals under 65 and unchanged fragility fractures in the elderly population.

A knowledge elicitation approach to traffic accident analysis in open data: comparing periods before and after the Covid-19 outbreak

Extracting knowledge from open data of traffic accidents has been attracting increasing attention to policymakers responsible for road safety. This article presents a knowledge elicitation approach to exploring the determinants of traffic accidents from open government data of an urban area in Taiwan. The collected open dataset contains 34 decisional attributes and one predictive attribute (i.e., type of injury, including head, breast, leg), and 47,974 cases. Prediction models using a classification-oriented mechanism and generated rules that considered datasets from before (B-dataset; 30,116 cases) and after (A-dataset; 17,868 cases) beginning to combat the Covid-19 pandemic in an urban area of Taiwan were compared. The findings showed that prediction accuracy was acceptable but not high, at 70.73% for B-dataset and 74.77% for A-dataset. Determinants in the human and vehicle categories revealed higher classification ranks than those in the temporal and environment categories. Traffic accidents involving motorcycles were 5.13% higher in A-dataset, whereas those involving cars were 4.11% lower. Injury on leg or foot was 3.46% higher in A-dataset, whereas other types of injury were up to 1.00% lower. The average support for rules in the A-dataset rule base and the simplicity of the A-dataset decision tree were higher than those of B-dataset. The research demonstrates the value of open government data in prediction model development and knowledge elicitation to support policymaking in the traffic safety domain.

The comparative analysis of SARIMA, Facebook Prophet, and LSTM for road traffic injury prediction in Northeast China

Objective

This cross-sectional research aims to develop reliable predictive short-term prediction models to predict the number of RTIs in Northeast China through comparative studies.

Methodology

Seasonal auto-regressive integrated moving average (SARIMA), Long Short-Term Memory (LSTM), and Facebook Prophet (Prophet) models were used for time series prediction of the number of RTIs inpatients. The three models were trained using data from 2015 to 2019, and their prediction accuracy was compared using data from 2020 as a test set. The parameters of the SARIMA model were determined using the autocorrelation function (ACF) and the partial autocorrelation function (PACF). The LSTM uses linear as the activation function, the mean square error (MSE) as the loss function and the Adam optimizer to construct the model, while the Prophet model is built on the Python platform. The root mean squared error (RMSE), mean absolute error (MAE) and Mean Absolute Percentage Error (MAPE) are used to measure the predictive performance of the model.

Findings

In this research, the LSTM model had the highest prediction accuracy, followed by the Prophet model, and the SARIMA model had the lowest prediction accuracy. The trend in medical expenditure of RTIs inpatients overlapped highly with the number of RTIs inpatients.

Conclusion

By adjusting the activation function and optimizer, the LSTM predicts the number of RTIs inpatients more accurately and robustly than other models. Compared with other models, LSTM models still show excellent prediction performance in the face of data with seasonal and drastic changes. The LSTM can provide a better basis for planning and management in healthcare administration.

Implication

The results of this research show that it is feasible to accurately forecast the demand for healthcare resources with seasonal distribution using a suitable forecasting model. The prediction of specific medical service volumes will be an important basis for medical management to allocate medical and health resources.